contributor author | Liu, Tianyuan | |
contributor author | Bao, Jinsong | |
contributor author | Wang, Junliang | |
contributor author | Zhang, Yiming | |
date accessioned | 2022-02-04T14:23:52Z | |
date available | 2022-02-04T14:23:52Z | |
date copyright | 2020/01/03/ | |
date issued | 2020 | |
identifier issn | 1530-9827 | |
identifier other | jcise_20_2_021005.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4273578 | |
description abstract | Machine vision has a wide range of applications in the field of welding. The rise of convolutional neural network (CNN) provides a new way to extract visual features of welding. Due to the limitation of the small size of our molten pool dataset, the regularization of the CNN model is necessary to prevent overfitting. We propose a coarse-grained regularization method for convolution kernels (CGRCKs), which is designed to maximize the difference between convolution kernels in the same layer. The algorithm performance was tested on our self-made dataset and other public datasets. The results show that the CGRCK method can extract multi-faceted features. Compared with L1 or L2 regularization, the proposed method works great on CNNs and introduces little overhead cost to the training. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification | |
type | Journal Paper | |
journal volume | 20 | |
journal issue | 2 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4045294 | |
page | 21005 | |
tree | Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002 | |
contenttype | Fulltext | |